Hermes 4 70B (Reasoning)

Given Hermes 4 70B's exceptional intelligence score and 'Reasoning' variant tag, it's best utilized for tasks that genuinely require advanced analytical capabilities. Deploying it for simple tasks like basic text rephrasing or short Q&A might be an overspend compared to smaller, less capable models.

• **Focus:** Complex problem-solving, detailed analysis, multi-step reasoning, code generation, and intricate summarization.
• **Avoid:** Trivial text generation, simple chatbots, or tasks where a smaller, cheaper model would suffice.

The model's evaluation showed a tendency for higher verbosity. While this can be beneficial for detailed responses, it directly impacts output token costs. Implement strategies to control the length of generated responses.

• **Prompt Engineering:** Use explicit instructions like "be concise," "limit response to X sentences," or "provide only the answer, no preamble."
• **Post-processing:** Implement automated truncation or summarization of model outputs if full verbosity isn't always necessary.
• **Iterative Generation:** For very long outputs, consider generating in chunks to maintain control and review intermediate costs.

As an open-source model, Hermes 4 70B offers long-term cost advantages through potential self-hosting or alternative provider options. While Nebius (FP8) provides a strong starting point, keep an eye on the evolving ecosystem.

• **Community Support:** Engage with the Nous Research community for optimization tips and shared best practices.
• **Future Deployment:** Evaluate self-hosting options as your usage scales, potentially reducing per-token costs by amortizing infrastructure.
• **Provider Diversification:** Monitor other providers who may offer Hermes 4 70B, comparing their pricing and performance against Nebius.

With a 128k context window, Hermes 4 70B can handle vast amounts of input. However, every input token costs money. Ensure that your prompts are efficient and only include necessary information.

• **Retrieval Augmented Generation (RAG):** Use RAG to fetch only the most relevant information for the model, rather than feeding it entire documents.
• **Context Compression:** Explore techniques to condense or summarize input context before passing it to the model, especially for very long documents.
• **Dynamic Context:** Adjust the amount of context provided based on the complexity of the query, using less for simpler questions.

Hermes 4 70B (Reasoning) is an advanced large language model developed by Nous Research, based on the Llama-3.1 70B architecture. It is specifically tuned for complex reasoning tasks and is notable for its high intelligence score and open-source license.

It scores 39 on the Artificial Analysis Intelligence Index, placing it significantly above the average of 26 for comparable models. This indicates its strong capability in understanding and processing complex information for reasoning tasks.

The model features a substantial context window of 128,000 tokens. This allows it to process and maintain context over very long inputs, making it suitable for detailed analysis and long-form content generation.

On Nebius (FP8), the input token price is $0.13 per 1 million tokens, and the output token price is $0.40 per 1 million tokens. These prices are generally competitive and often below the average for models of similar capability.

Yes, Hermes 4 70B (Reasoning) operates under an open license. This provides users with significant flexibility for deployment, customization, and integration into various applications without proprietary restrictions.

It achieves a median output speed of 85 tokens per second on Nebius (FP8), which is faster than the average of 72 tokens per second for comparable models. Its latency (time to first token) is also low at 0.61 seconds.

FP8 refers to an 8-bit floating-point quantization. This technique reduces the precision of the model's weights and activations to 8 bits, which significantly lowers memory usage and increases inference speed, often with minimal impact on accuracy for many tasks.